Browsing by Author "Kelsoe, John"

Now showing 1 - 3 of 3
  • Thumbnail Image
    Item
    Detecting significant genotype-phenotype association rules in bipolar disorder: market research meets complex genetics
    (SpringerOpen, 2018-11-11) Breuer, René; Mattheisen, Manuel; Frank, Josef; Krumm, Bertram; Treutlein, Jens; Kassem, Layla; Strohmaier, Jana; Herms, Stefan; Mühleisen, Thomas W.; Degenhardt, Franziska; Cichon, Sven; Nöthen, Markus M.; Karypis, George; Kelsoe, John; Greenwood, Tiffany; Nievergelt, Caroline; Shilling, Paul; Shekhtman, Tatyana; Edenberg, Howard; Craig, David; Szelinger, Szabolcs; Nurnberger, John; Gershon, Elliot; Alliey‑Rodriguez, Ney; Zandi, Peter; Goes, Fernando; Schork, Nicholas; Smith, Erin; Koller, Daniel; Zhang, Peng; Badner, Judith; Berrettini, Wade; Bloss, Cinnamon; Byerley, William; Coryell, William; Foroud, Tatiana; Guo, Yirin; Hipolito, Maria; Keating, Brendan; Lawson, William; Liu, Chunyu; Mahon, Pamela; McInnis, Melvin; Murray, Sarah; Nwulia, Evaristus; Potash, James; Rice, John; Scheftner, William; Zöllner, Sebastian; McMahon, Francis J.; Rietschel, Marcella; Schulze, Thomas G.; Biochemistry and Molecular Biology, School of Medicine
    BACKGROUND: Disentangling the etiology of common, complex diseases is a major challenge in genetic research. For bipolar disorder (BD), several genome-wide association studies (GWAS) have been performed. Similar to other complex disorders, major breakthroughs in explaining the high heritability of BD through GWAS have remained elusive. To overcome this dilemma, genetic research into BD, has embraced a variety of strategies such as the formation of large consortia to increase sample size and sequencing approaches. Here we advocate a complementary approach making use of already existing GWAS data: a novel data mining procedure to identify yet undetected genotype-phenotype relationships. We adapted association rule mining, a data mining technique traditionally used in retail market research, to identify frequent and characteristic genotype patterns showing strong associations to phenotype clusters. We applied this strategy to three independent GWAS datasets from 2835 phenotypically characterized patients with BD. In a discovery step, 20,882 candidate association rules were extracted. RESULTS: Two of these rules-one associated with eating disorder and the other with anxiety-remained significant in an independent dataset after robust correction for multiple testing. Both showed considerable effect sizes (odds ratio ~ 3.4 and 3.0, respectively) and support previously reported molecular biological findings. CONCLUSION: Our approach detected novel specific genotype-phenotype relationships in BD that were missed by standard analyses like GWAS. While we developed and applied our method within the context of BD gene discovery, it may facilitate identifying highly specific genotype-phenotype relationships in subsets of genome-wide data sets of other complex phenotype with similar epidemiological properties and challenges to gene discovery efforts.
  • Thumbnail Image
    Item
    Lithium alters expression of RNAs in a type-specific manner in differentiated human neuroblastoma neuronal cultures, including specific genes involved in Alzheimer’s disease
    (Nature Research, 2019-12-04) Maloney, Bryan; Balaraman, Yokesh; Liu, Yunlong; Chopra, Nipun; Edenberg, Howard J.; Kelsoe, John; Nurnberger, John I.; Lahiri, Debomoy K.; Psychiatry, School of Medicine
    Lithium (Li) is a medication long-used to treat bipolar disorder. It is currently under investigation for multiple nervous system disorders, including Alzheimer’s disease (AD). While perturbation of RNA levels by Li has been previously reported, its effects on the whole transcriptome has been given little attention. We, therefore, sought to determine comprehensive effects of Li treatment on RNA levels. We cultured and differentiated human neuroblastoma (SK-N-SH) cells to neuronal cells with all-trans retinoic acid (ATRA). We exposed cultures for one week to lithium chloride or distilled water, extracted total RNA, depleted ribosomal RNA and performed whole-transcriptome RT-sequencing. We analyzed results by RNA length and type. We further analyzed expression and protein interaction networks between selected Li-altered protein-coding RNAs and common AD-associated gene products. Lithium changed expression of RNAs in both non-specific (inverse to sequence length) and specific (according to RNA type) fashions. The non-coding small nucleolar RNAs (snoRNAs) were subject to the greatest length-adjusted Li influence. When RNA length effects were taken into account, microRNAs as a group were significantly less likely to have had levels altered by Li treatment. Notably, several Li-influenced protein-coding RNAs were co-expressed or produced proteins that interacted with several common AD-associated genes and proteins. Lithium’s modification of RNA levels depends on both RNA length and type. Li activity on snoRNA levels may pertain to bipolar disorders while Li modification of protein coding RNAs may be relevant to AD.
  • Thumbnail Image
    Item
    Sex-Dependent Shared and Non-Shared Genetic Architecture Across Mood and Psychotic Disorders
    (Elsevier, 2022) Blokland, Gabriëlla A. M.; Grove, Jakob; Chen, Chia-Yen; Cotsapas, Chris; Tobet, Stuart; Handa, Robert; Schizophrenia Working Group of the Psychiatric Genomics Consortium; St. Clair, David; Lencz, Todd; Mowry, Bryan J.; Periyasamy, Sathish; Cairns, Murray J.; Tooney, Paul A.; Wu, Jing Qin; Kelly, Brian; Kirov, George; Sullivan, Patrick F.; Corvin, Aiden; Riley, Brien P.; Esko, Tõnu; Milani, Lili; Jönsson, Erik G.; Palotie, Aarno; Ehrenreich, Hannelore; Begemann, Martin; Steixner-Kumar, Agnes; Sham, Pak C.; Iwata, Nakao; Weinberger, Daniel R.; Gejman, Pablo V.; Sanders, Alan R.; Buxbaum, Joseph D.; Rujescu, Dan; Giegling, Ina; Konte, Bettina; Hartmann, Annette M.; Bramon, Elvira; Murray, Robin M.; Pato, Michele T.; Lee, Jimmy; Melle, Ingrid; Molden, Espen; Ophoff, Roel A.; McQuillin, Andrew; Bass, Nicholas J.; Adolfsson, Rolf; Malhotra, Anil K.; Bipolar Disorder Working Group of the Psychiatric Genomics Consortium; Martin, Nicholas G.; Fullerton, Janice M.; Mitchell, Philip B.; Schofield, Peter R.; Forstner, Andreas J.; Degenhardt, Franziska; Schaupp, Sabrina; Comes, Ashley L.; Kogevinas, Manolis; Guzman-Parra, José; Reif, Andreas; Streit, Fabian; Sirignano, Lea; Cichon, Sven; Grigoroiu-Serbanescu, Maria; Hauser, Joanna; Lissowska, Jolanta; Mayoral, Fermin; Müller-Myhsok, Bertram; Świątkowska, Beata; Schulze, Thomas G.; Nöthen, Markus M.; Rietschel, Marcella; Kelsoe, John; Leboyer, Marion; Jamain, Stéphane; Etain, Bruno; Bellivier, Frank; Vincent, John B.; Alda, Martin; O'Donovan, Claire; Cervantes, Pablo; Biernacka, Joanna M.; Frye, Mark; McElroy, Susan L.; Scott, Laura J.; Stahl, Eli A.; Landén, Mikael; Hamshere, Marian L.; Smeland, Olav B.; Djurovic, Srdjan; Vaaler, Arne E.; Andreassen, Ole A.; Major Depressive Disorder Working Group of the Psychiatric Genomics Consortium; Baune, Bernhard T.; Air, Tracy; Preisig, Martin; Uher, Rudolf; Levinson, Douglas F.; Weissman, Myrna M.; Potash, James B.; Shi, Jianxin; Knowles, James A.; Perlis, Roy H.; Lucae, Susanne; Boomsma, Dorret I.; Penninx, Brenda W. J. H.; Hottenga, Jouke-Jan; de Geus, Eco J. C.; Willemsen, Gonneke; Milaneschi, Yuri; Tiemeier, Henning; Grabe, Hans J.; Teumer, Alexander; Van der Auwera, Sandra; Völker, Uwe; Hamilton, Steven P.; Magnusson, Patrik K. E.; Viktorin, Alexander; Mehta, Divya; Mullins, Niamh; Adams, Mark J.; Breen, Gerome; McIntosh, Andrew M.; Lewis, Cathryn M.; Sex Differences Cross-Disorder Analysis Group of the Psychiatric Genomics Consortium; iPSYCH; Hougaard, David M.; Nordentoft, Merete; Mors, Ole; Mortensen, Preben B.; Werge, Thomas; Als, Thomas D.; Børglum, Anders D.; Petryshen, Tracey L.; Smoller, Jordan W.; Goldstein, Jill M.; Psychiatry, School of Medicine
    Background: Sex differences in incidence and/or presentation of schizophrenia (SCZ), major depressive disorder (MDD), and bipolar disorder (BIP) are pervasive. Previous evidence for shared genetic risk and sex differences in brain abnormalities across disorders suggest possible shared sex-dependent genetic risk. Methods: We conducted the largest to date genome-wide genotype-by-sex (G×S) interaction of risk for these disorders using 85,735 cases (33,403 SCZ, 19,924 BIP, and 32,408 MDD) and 109,946 controls from the PGC (Psychiatric Genomics Consortium) and iPSYCH. Results: Across disorders, genome-wide significant single nucleotide polymorphism-by-sex interaction was detected for a locus encompassing NKAIN2 (rs117780815, p = 3.2 × 10-8), which interacts with sodium/potassium-transporting ATPase (adenosine triphosphatase) enzymes, implicating neuronal excitability. Three additional loci showed evidence (p < 1 × 10-6) for cross-disorder G×S interaction (rs7302529, p = 1.6 × 10-7; rs73033497, p = 8.8 × 10-7; rs7914279, p = 6.4 × 10-7), implicating various functions. Gene-based analyses identified G×S interaction across disorders (p = 8.97 × 10-7) with transcriptional inhibitor SLTM. Most significant in SCZ was a MOCOS gene locus (rs11665282, p = 1.5 × 10-7), implicating vascular endothelial cells. Secondary analysis of the PGC-SCZ dataset detected an interaction (rs13265509, p = 1.1 × 10-7) in a locus containing IDO2, a kynurenine pathway enzyme with immunoregulatory functions implicated in SCZ, BIP, and MDD. Pathway enrichment analysis detected significant G×S interaction of genes regulating vascular endothelial growth factor receptor signaling in MDD (false discovery rate-corrected p < .05). Conclusions: In the largest genome-wide G×S analysis of mood and psychotic disorders to date, there was substantial genetic overlap between the sexes. However, significant sex-dependent effects were enriched for genes related to neuronal development and immune and vascular functions across and within SCZ, BIP, and MDD at the variant, gene, and pathway levels.